Zep addresses one of the hardest challenges in building production AI agents: assembling the right context at the right time. Rather than relying on simple retrieval-augmented generation, Zep maintains a temporal knowledge graph that continuously ingests conversations, business data, documents, and events, automatically extracting entities and relationships while tracking how they change over time. This means agents can understand not just what happened, but when it happened, who was involved, and how the situation evolved — enabling much more nuanced and contextually aware responses.
The platform delivers pre-assembled context blocks specifically formatted for LLM consumption with sub-200ms latency, eliminating the complex orchestration that teams typically build in-house. Zep supports multiple retrieval strategies including graph-based relationship traversal, semantic search, and temporal queries, combining results into coherent context windows. It integrates natively with major agent frameworks including LangChain, LlamaIndex, AutoGen, and Google Agent Development Kit, with official SDKs available for Python, TypeScript, and Go.
Backed by a $17M Series A, Zep has built a focused product for enterprise context engineering with SOC 2 Type 2 and HIPAA compliance certifications. The open-source repository on GitHub hosts examples, integrations, an MCP server, and evaluation tools, while the core platform runs as Zep Cloud — a managed service with a free tier for development. With 4,400+ GitHub stars and integrations across the major agent ecosystem, Zep is particularly well-suited for teams building customer-facing agents, enterprise assistants, or any application where rich conversational memory and relationship awareness are critical.
